José Miguel Hernández-Lobato

ORCID

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 173

Top Venues

Publications

Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta
Image Reconstruction via Deep Image Prior Subspaces. Trans. Mach. Learn. Res. 2024 (2024)
Elre Talea Oldewage, Ross M. Clarke, José Miguel Hernández-Lobato
Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks. Trans. Mach. Learn. Res. 2024 (2024)
Jiajun He, Gergely Flamich, Zongyu Guo, José Miguel Hernández-Lobato
RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations. ICLR (2024)
Xuexin Chen, Ruichu Cai, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, José Miguel Hernández-Lobato
Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation. CoRR (2024)
Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato
Retro-fallback: retrosynthetic planning in an uncertain world. ICLR (2024)
Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, José Miguel Hernández-Lobato
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes. CoRR (2024)
Paulina Kulyte, Francisco Vargas, Simon Valentin Mathis, Yu Guang Wang, José Miguel Hernández-Lobato, Pietro Liò
Improving Antibody Design with Force-Guided Sampling in Diffusion Models. CoRR (2024)
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
Stochastic Gradient Descent for Gaussian Processes Done Right. ICLR (2024)
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR (2024)
Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio
Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR (2024)
Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato
Accelerating Relative Entropy Coding with Space Partitioning. CoRR (2024)
Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
Diffusive Gibbs Sampling. CoRR (2024)
Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. CoRR (2024)
James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
A Generative Model of Symmetry Transformations. CoRR (2024)
Pablo Morales-Álvarez, Arne Schmidt, José Miguel Hernández-Lobato, Rafael Molina
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images. Pattern Recognit. 146 (2024)
Austin Tripp, José Miguel Hernández-Lobato
Diagnosing and fixing common problems in Bayesian optimization for molecule design. CoRR (2024)
Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato
Compression with Bayesian Implicit Neural Representations. NeurIPS (2023)
Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato
Retro-fallback: retrosynthetic planning in an uncertain world. CoRR (2023)
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. CoRR (2023)
Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato
normflows: A PyTorch Package for Normalizing Flows. CoRR (2023)
Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato
SE(3) Equivariant Augmented Coupling Flows. CoRR (2023)
Richard Bergna, Felix L. Opolka, Pietro Liò, José Miguel Hernández-Lobato
Graph Neural Stochastic Differential Equations. CoRR (2023)
Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan
Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. 2023 (2023)
Pablo Morales-Álvarez, Arne Schmidt, José Miguel Hernández-Lobato, Rafael Molina
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images. CoRR (2023)
Elre T. Oldewage, Ross M. Clarke, José Miguel Hernández-Lobato
Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks. CoRR (2023)
Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior. Trans. Mach. Learn. Res. 2023 (2023)
Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
Faster Relative Entropy Coding with Greedy Rejection Coding. CoRR (2023)
Jiajun He, Gergely Flamich, Zongyu Guo, José Miguel Hernández-Lobato
RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations. CoRR (2023)
Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
Faster Relative Entropy Coding with Greedy Rejection Coding. NeurIPS (2023)
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR (2023)
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato
Flow Annealed Importance Sampling Bootstrap. ICLR (2023)
Wenlin Chen, Julien Horwood, Juyeon Heo, José Miguel Hernández-Lobato
Leveraging Task Structures for Improved Identifiability in Neural Network Representations. CoRR (2023)
Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato
Compression with Bayesian Implicit Neural Representations. CoRR (2023)
Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato
Tanimoto Random Features for Scalable Molecular Machine Learning. CoRR (2023)
Jihao Andreas Lin, Javier Antorán, José Miguel Hernández-Lobato
Online Laplace Model Selection Revisited. CoRR (2023)
Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato
normflows: A PyTorch Package for Normalizing Flows. J. Open Source Softw. 8 (87) (2023)
Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato
Tanimoto Random Features for Scalable Molecular Machine Learning. NeurIPS (2023)
Austin Tripp, José Miguel Hernández-Lobato
Genetic algorithms are strong baselines for molecule generation. CoRR (2023)
Szilvia Ujváry, Gergely Flamich, Vincent Fortuin, José Miguel Hernández-Lobato
Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo. CoRR (2023)
Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato
SE(3) Equivariant Augmented Coupling Flows. NeurIPS (2023)
Ross M. Clarke, Baiyu Su, José Miguel Hernández-Lobato
Adam through a Second-Order Lens. CoRR (2023)
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. NeurIPS (2023)
Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. ICLR (2023)
Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
Sampling-based inference for large linear models, with application to linearised Laplace. ICLR (2023)
Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Zeljko Kereta, Bangti Jin
Fast and Painless Image Reconstruction in Deep Image Prior Subspaces. CoRR (2023)
Jihao Andreas Lin, Gergely Flamich, José Miguel Hernández-Lobato
Minimal Random Code Learning with Mean-KL Parameterization. CoRR (2023)
Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin
A Probabilistic Deep Image Prior for Computational Tomography. CoRR (2022)
Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp, José Miguel Hernández-Lobato, Andreas Bender, Sergio Bacallado
DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design. J. Chem. Inf. Model. 62 (15) (2022)
Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. CoRR (2022)
Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf
Invariant Causal Representation Learning for Out-of-Distribution Generalization. ICLR (2022)
Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
Sampling-based inference for large linear models, with application to linearised Laplace. CoRR (2022)
Ross M. Clarke, Elre Talea Oldewage, José Miguel Hernández-Lobato
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation. ICLR (2022)
Ignacio Peis, Chao Ma, José Miguel Hernández-Lobato
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo. NeurIPS (2022)
Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. ICML (2022)
Weijie He, Xiaohao Mao, Chao Ma, Yu Huang, José Miguel Hernández-Lobato, Ting Chen
BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis. WWW (2022)
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato
Flow Annealed Importance Sampling Bootstrap. CoRR (2022)
Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
Fast Relative Entropy Coding with A* coding. ICML (2022)
Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang
Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML (2022)
Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
Fast Relative Entropy Coding with A* coding. CoRR (2022)
Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. CoRR (2022)
Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato
Resampling Base Distributions of Normalizing Flows. AISTATS (2022)
Riccardo Barbano, Johannes Leuschner, Javier Antorán, Bangti Jin, José Miguel Hernández-Lobato
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior. CoRR (2022)
Ignacio Peis, Chao Ma, José Miguel Hernández-Lobato
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo. CoRR (2022)
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR (2021)
Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. CoRR (2021)
Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato
Activation-level uncertainty in deep neural networks. ICLR (2021)
Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Depth Uncertainty Networks for Active Learning. CoRR (2021)
Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang
Action-Sufficient State Representation Learning for Control with Structural Constraints. CoRR (2021)
Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
Sliced Kernelized Stein Discrepancy. ICLR (2021)
Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal
Improving black-box optimization in VAE latent space using decoder uncertainty. CoRR (2021)
Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon, José Miguel Hernández-Lobato
Sampling the Variational Posterior with Local Refinement. Entropy 23 (11) (2021)
Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Bayesian Deep Learning via Subnetwork Inference. ICML (2021)
Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang
Contextual HyperNetworks for Novel Feature Adaptation. CoRR (2021)
Ross M. Clarke, Elre T. Oldewage, José Miguel Hernández-Lobato
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation. CoRR (2021)
Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Richard G. Baraniuk, Cheng Zhang
Educational Question Mining At Scale: Prediction, Analysis and Personalization. AAAI (2021)
Chao Ma, José Miguel Hernández-Lobato
Functional Variational Inference based on Stochastic Process Generators. NeurIPS (2021)
Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato
Predictive Complexity Priors. AISTATS (2021)
Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato
Active Slices for Sliced Stein Discrepancy. CoRR (2021)
Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato
Getting a CLUE: A Method for Explaining Uncertainty Estimates. ICLR (2021)
Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf
Nonlinear Invariant Risk Minimization: A Causal Approach. CoRR (2021)
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, José Miguel Hernández-Lobato
Bootstrap Your Flow. CoRR (2021)
Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández-Lobato
Symmetry-Aware Actor-Critic for 3D Molecular Design. ICLR (2021)
Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal
Improving black-box optimization in VAE latent space using decoder uncertainty. NeurIPS (2021)
Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato
Active Slices for Sliced Stein Discrepancy. ICML (2021)
Andrew Campbell, Wenlong Chen, Vincent Stimper, José Miguel Hernández-Lobato, Yichuan Zhang
A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization. ICML (2021)
Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato
Resampling Base Distributions of Normalizing Flows. CoRR (2021)
Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. ICBINB@NeurIPS (2021)
Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp, José Miguel Hernández-Lobato, Andreas Bender, Sergio Bacallado
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design. CoRR (2021)
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
Barking up the right tree: an approach to search over molecule synthesis DAGs. CoRR (2020)
Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
Depth Uncertainty in Neural Networks. NeurIPS (2020)
Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. NeurIPS (2020)
Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato
Getting a CLUE: A Method for Explaining Uncertainty Estimates. CoRR (2020)
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR (2020)
Jonathan Gordon, José Miguel Hernández-Lobato
Combining deep generative and discriminative models for Bayesian semi-supervised learning. Pattern Recognit. 100 (2020)
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. NeurIPS (Competition and Demos) (2020)
Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
Sliced Kernelized Stein Discrepancy. CoRR (2020)
Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, Bernhard Schölkopf
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation. CoRR (2020)
Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding. CoRR (2020)
Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
Depth Uncertainty in Neural Networks. CoRR (2020)
Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
Variational Depth Search in ResNets. CoRR (2020)

José Miguel Hernández-Lobato.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

José Miguel Hernández-Lobato